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Tier-1 brand and metro location increase competition, though niche agent/LLM specialization moderates applicant density.
Deep LLM, agent orchestration, and vector DB expertise creates high domain specificity and limited cross-industry transferability.
Mandatory 5+ years plus specific LLM, vector DB, cloud, DevOps, and governance requirements make hiring filters very strict.
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Lead design and implementation of LLM-driven AI agent services for software development lifecycle (SDLC) including code generation, testing, and observability on AWS.
Develop orchestration and communication layers between AI agents using frameworks like A2A SDK, LangGraph, and Auto Gen integrated with developer toolchains (Jira, Bitbucket, GitHub, Terraform).
Provide technical leadership and governance in adoption of AI-assisted engineering tools to improve code quality, delivery speed, and operational outcomes across teams.
5+ years of applied software engineering experience with formal training or certification in software engineering concepts.
Proficient in Python, Pydantic, FastAPI, LangGraph, and Vector Databases for building AI agent solutions deployed on AWS technologies including EKS, Lambda, S3, and Terraform.
Experience integrating LLMs and AI agent frameworks (Langchain, LangGraph, Autogen, MCPs, A2A) with CI/CD, Kubernetes, Docker, and API knowledge.
Demonstrated leadership in deploying enterprise-authorized AI-assisted software development tools with responsible AI practices and security compliance.
Experienced software engineer with deep technical expertise in AI-driven SDLC automation and multi-agent system orchestration on cloud environments, especially AWS.
Strong focus on engineering governance, including secure and compliant AI tool adoption, validation of AI output, and measurable improvements in development workflows.
Capable of mentoring senior engineers and leading teams in technical design and operationalizing AI-assisted development frameworks in large enterprise settings.